Optimizing Customer Support in Ride-Hailing Platforms: Leveraging Data-Driven Decision-Making and Workflow Automation

Authors

Fnu Nagarajan

Abstract

The ride-hailing industry has rapidly transformed urban mobility, offering convenient, on-demand transportation services to millions of users. However, as the industry has grown, customer expectations regarding service quality, reliability, and support have increased. Efficient customer support plays a critical role in retaining users and maintaining a competitive edge. Traditional support models, which rely heavily on manual intervention, often result in delays, inconsistencies, and inefficiencies. To address these challenges, ride-hailing platforms have embraced workflow automation and data-driven decision-making, enabling predictive customer service, AI-powered chatbots, and automated issue resolution. This paper explores the role of AI, machine learning, and workflow automation in optimizing customer support, with an in-depth case study on Lyft’s help portal revamp. The discussion extends to implementation challenges, ethical considerations, and future trends shaping AI-driven customer service. By integrating advanced analytics, proactive customer assistance, and seamless automation workflows, ride-hailing companies can significantly enhance service efficiency and user satisfaction.

Keywords

Customer Support, Ride-Hailing, Data-Driven Decision Making, Workflow Automation, AI in Customer Service, Chatbots, Self-Service, Predictive Analytics.

Optimizing Customer Support in Ride-Hailing Platforms: Leveraging Data-Driven Decision-Making and Workflow Automation. Fnu Nagarajan. 2023. IJIRCT, Volume 9, Issue 6. Pages 1-4. https://www.ijirct.org/viewPaper.php?paperId=2503092